library(ggplot2)
library(RVenn)
# load the data and visualization functions
source("global.R")
source("../utils/visualization_functions.R")
Normalised spectral count per group and total, add columns to dataframe
# Z-score normalisation, by group and total
all_data_dicty <- all_data_dicty %>%
group_by(bait, condition) %>%
mutate(grouped_z_score_spectral = scale(spectral_count))
all_data_dicty <- all_data_dicty %>%
mutate(z_score_spectral = scale(spectral_count))
Summary statistics Dicty
all_data_dicty %>%
summarise(Unique_bait = n_distinct(bait))
## `summarise()` regrouping output by 'bait' (override with `.groups` argument)
## # A tibble: 15 x 3
## # Groups: bait [12]
## bait condition Unique_bait
## <fct> <fct> <int>
## 1 Galpha2 starv 1
## 2 Galpha2 veg 1
## 3 Galpha4 Normal 1
## 4 Galpha8 GDP 1
## 5 Galpha8 GppNHp 1
## 6 Galpha8 Normal 1
## 7 Gbeta1 Normal 1
## 8 Gbeta2 Normal 1
## 9 Rac1 Normal 1
## 10 RapA Normal 1
## 11 RasB Normal 1
## 12 RasC Normal 1
## 13 RasG1 Normal 1
## 14 Ric8 Normal 1
## 15 Roco4 BACKGROUND 1
Show the unique baits count
all_data_dicty %>%
group_by(bait, condition) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = '_') %>%
summarise(Unique_bait_condition = n_distinct(bait_condition))
## # A tibble: 1 x 1
## Unique_bait_condition
## <int>
## 1 15
Show how many instances (lines) of bait there are
table(all_data_dicty$bait)
##
## Galpha2 Galpha4 Galpha8 Gbeta1 Gbeta2 Rac1 RapA RasB RasC RasG1
## 3206 1603 4100 876 876 1448 461 808 998 337
## Ric8 Roco4
## 1448 1448
Show how many instances (lines) of condition there are
table(all_data_dicty$condition)
##
## BACKGROUND GDP GppNHp Normal starv veg
## 1448 1436 1436 10083 1603 1603
Show total spectral count per bait + condition
all_data_dicty %>%
group_by(bait, condition) %>%
summarise(.groups = 'drop', across(spectral_count, sum))
## # A tibble: 15 x 3
## bait condition spectral_count
## <fct> <fct> <dbl>
## 1 Galpha2 starv 20155
## 2 Galpha2 veg 30986
## 3 Galpha4 Normal 27070
## 4 Galpha8 GDP 13099
## 5 Galpha8 GppNHp 23136
## 6 Galpha8 Normal 23324
## 7 Gbeta1 Normal 16390
## 8 Gbeta2 Normal 20644
## 9 Rac1 Normal 22196
## 10 RapA Normal 5238
## 11 RasB Normal 12182
## 12 RasC Normal 22778
## 13 RasG1 Normal 12369
## 14 Ric8 Normal 18323
## 15 Roco4 BACKGROUND 19376
Show normalized spectral count per bait + condition
all_data_dicty %>%
group_by(bait, condition) %>%
summarise(.groups = 'drop', across(z_score_spectral, sum))
## # A tibble: 15 x 3
## bait condition z_score_spectral
## <fct> <fct> <dbl>
## 1 Galpha2 starv 9.37e-16
## 2 Galpha2 veg 1.30e-14
## 3 Galpha4 Normal 7.91e-16
## 4 Galpha8 GDP -2.50e-14
## 5 Galpha8 GppNHp 4.31e-14
## 6 Galpha8 Normal 1.87e-14
## 7 Gbeta1 Normal 1.72e-14
## 8 Gbeta2 Normal 7.63e-15
## 9 Rac1 Normal 7.24e-15
## 10 RapA Normal -1.07e-14
## 11 RasB Normal 1.05e-14
## 12 RasC Normal -2.95e-15
## 13 RasG1 Normal -4.02e-15
## 14 Ric8 Normal -4.04e-15
## 15 Roco4 BACKGROUND 1.28e-15
# this does not look correct, we should normalize by total spectral count?, can we sum z-scores?
Show how many uniq uniprot id’s are in the set (dicty has 6 chromosomes and 12500 proteins)
n_distinct(all_data_dicty$uniprot)
## [1] 2988
uniq proteins per bait_condition
bait_condition <- group_by(all_data_dicty, bait, condition)
bait_condition %>%
summarise(n_distinct(uniprot))
## `summarise()` regrouping output by 'bait' (override with `.groups` argument)
## # A tibble: 15 x 3
## # Groups: bait [12]
## bait condition `n_distinct(uniprot)`
## <fct> <fct> <int>
## 1 Galpha2 starv 1603
## 2 Galpha2 veg 1603
## 3 Galpha4 Normal 1603
## 4 Galpha8 GDP 1436
## 5 Galpha8 GppNHp 1436
## 6 Galpha8 Normal 1228
## 7 Gbeta1 Normal 876
## 8 Gbeta2 Normal 876
## 9 Rac1 Normal 1448
## 10 RapA Normal 461
## 11 RasB Normal 808
## 12 RasC Normal 998
## 13 RasG1 Normal 337
## 14 Ric8 Normal 1448
## 15 Roco4 BACKGROUND 1448
Unique proteins per condition
group_by(all_data_dicty, condition) %>%
summarise(n_distinct(uniprot))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 6 x 2
## condition `n_distinct(uniprot)`
## <fct> <int>
## 1 BACKGROUND 1448
## 2 GDP 1436
## 3 GppNHp 1436
## 4 Normal 2933
## 5 starv 1603
## 6 veg 1603
Unique proteins per bait
group_by(all_data_dicty, bait) %>%
summarise(n_distinct(uniprot))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 12 x 2
## bait `n_distinct(uniprot)`
## <fct> <int>
## 1 Galpha2 1603
## 2 Galpha4 1603
## 3 Galpha8 1446
## 4 Gbeta1 876
## 5 Gbeta2 876
## 6 Rac1 1448
## 7 RapA 461
## 8 RasB 808
## 9 RasC 998
## 10 RasG1 337
## 11 Ric8 1448
## 12 Roco4 1448
Barplot showing the total spectral count for each bait
p <- ggplot(all_data_dicty, aes(x=bait, y =spectral_count))
p <- p + geom_col() + labs(title = 'Spectral count vs bait')
p
Barplot showing the absolute spectral count for each condition
p <- ggplot(all_data_dicty, aes(x=condition, y =spectral_count))
p <- p + geom_col() + labs(title = 'Spectral count vs condition')
p
Combined spectral count for bait + condition, sum spectral count per group
my_data <-all_data_dicty %>% group_by(bait, condition) %>%
# .groups = drop is the default, will get warnings if you not specify explicitly
summarise(.groups = 'drop', across(spectral_count, sum)) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = "_")
p <- ggplot(my_data, aes(x = bait_condition, y = spectral_count))
p <- p + geom_col() + labs(title = 'spectral count vs bait + condition')
p + theme(axis.text.x = element_text(angle = 45, hjust = 1))
Barplot showing the average spectral count per protein for each bait divided the summed group count by the number of observations in that group so avg count per group
# get group sizes
group_spectra_normalized <- all_data_dicty %>%
group_by(bait) %>%
summarise(sum_spectra = sum(spectral_count), group_size = n(), average_spectra = sum(spectral_count/n()))
## `summarise()` ungrouping output (override with `.groups` argument)
p <- ggplot(group_spectra_normalized, aes(x=bait, y = average_spectra))
p <- p + geom_col() + labs(title = 'average Spectral count per bait')
p
Barplot showing the average spectral count for each condition: divided the summed group count by the number of observations in that group,
# get group sizes
group_spectra_normalized <- all_data_dicty %>%
group_by(condition) %>%
summarise(sum_spectra = sum(spectral_count), group_size = n(), average_spectra = sum(spectral_count/n()))
## `summarise()` ungrouping output (override with `.groups` argument)
p <- ggplot(group_spectra_normalized, aes(x=condition, y=average_spectra))
p <- p + geom_col() + labs(title = 'average Spectral count vs condition')
p
# get group sizes
group_spectra_normalized <- all_data_dicty %>%
group_by(bait, condition) %>%
summarise(sum_spectra = sum(spectral_count), group_size = n(), average_spectra = sum(spectral_count/n())) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = "_")
## `summarise()` regrouping output by 'bait' (override with `.groups` argument)
p <- ggplot(group_spectra_normalized, aes(x = bait_condition, y = average_spectra))
p <- p + geom_col() + labs(title = 'average spectral count vs bait + condition')
p + theme(axis.text.x = element_text(angle = 45, hjust = 1))
Combined spectral count for bait + condition, average spectral count per group
# get group sizes
group_spectra_normalized <- all_data_dicty %>%
group_by(bait, condition) %>%
summarise(sum_spectra = sum(spectral_count), group_size = n(), normalized_spectra = sum(spectral_count/n()), unique_proteins = n_distinct(uniprot)) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = "_")
## `summarise()` regrouping output by 'bait' (override with `.groups` argument)
p <- ggplot(group_spectra_normalized, aes(x = bait_condition, y = normalized_spectra))
p <- p + geom_col() + labs(title = 'average spectral count vs bait + condition') +
geom_text(aes(label=unique_proteins), vjust=0)
p + theme(axis.text.x = element_text(angle = 45, hjust = 1))
Boxplot absolute count vs bait
p <- ggplot(all_data_dicty, aes(x=bait, y =log10(spectral_count)))
p <- p + geom_boxplot(outlier.colour = "black") + labs(title = 'Spectral count vs bait')
p
## Warning: Removed 3896 rows containing non-finite values (stat_boxplot).
Boxplot count vs condition
p <- ggplot(all_data_dicty, aes(x=condition, y =log10(spectral_count)))
p <- p + geom_boxplot(outlier.colour = "black") + labs(title = 'Spectral count vs condition')
p
## Warning: Removed 3896 rows containing non-finite values (stat_boxplot).
Boxplot combined spectral count for bait + condition
my_data <-all_data_dicty %>%
group_by(bait, condition) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = "_")
my_data$bait_condition <- as.factor(my_data$bait_condition)
p <- ggplot(my_data, aes(x = bait_condition, y = log10(spectral_count)))
p <- p + geom_boxplot(outlier.colour = "black") + labs(title = 'spectral count vs bait + condition')
p + theme(axis.text.x = element_text(angle = 45, hjust = 1))
## Warning: Removed 3896 rows containing non-finite values (stat_boxplot).
p <- ggplot(all_data_dicty, aes(x=bait, y =log2(z_score_spectral)))
p <- p + geom_boxplot(outlier.colour = "black") + labs(title = 'normalized Spectral count vs bait')
p
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 14596 rows containing non-finite values (stat_boxplot).
boxplot count vs condition
p <- ggplot(all_data_dicty, aes(x=condition, y =log2(z_score_spectral)))
p <- p + geom_boxplot(outlier.colour = "black") + labs(title = 'normalized Spectral count vs condition')
p
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 14596 rows containing non-finite values (stat_boxplot).
my_data <-all_data_dicty %>%
group_by(bait, condition) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = "_")
my_data$bait_condition <- as.factor(my_data$bait_condition)
p <- ggplot(my_data, aes(x = bait_condition, y = log2(z_score_spectral)))
p <- p + geom_boxplot(outlier.colour = "black") + labs(title = 'normalized spectral count vs bait + condition')
p + theme(axis.text.x = element_text(angle = 45, hjust = 1))
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 14596 rows containing non-finite values (stat_boxplot).
bait <- split(all_data_dicty$uniprot, all_data_dicty$bait)
toy_bait = Venn(bait)
ggvenn(toy_bait, slice = c(1, 2,4))
condition <- split(all_data_dicty$uniprot, all_data_dicty$condition)
toy_condition = Venn(condition)
ggvenn(toy_condition, slice = c(1, 2,4))
my_data <-all_data_dicty %>% group_by(bait, condition) %>% tidyr::unite(bait_condition, c(bait, condition), sep = "_")
bait_condition <- split(my_data$uniprot, my_data$bait_condition)
toy = Venn(bait_condition)
#setmap(toy) # to many uniprot ids to show on the axis, needs filtering
ggvenn(toy, slice = c(1, 2,4))
# get character vector with overlapping proteins
overlap(toy, c("Galpha2_starv", "Galpha2_veg", "Galpha8_GDP", "Gbeta1_Normal"))
## [1] "GST" "sp|P0A6F5|CH60_ECOLI"
## [3] "sp|P0A6Y8|DNAK_ECOLI" "sp|P34042|GPA4_DICDI"
## [5] "sp|P07830|ACT1_DICDI" "gi|136429|sp|P00761|TRYP_PIG"
## [7] "tr|B0G141|B0G141_DICDI" "gi|7428712|pir||KRHU2"
## [9] "sp|Q556G3|GSTA2_DICDI" "gi|81175178|sp|P35527|K1C9_HUMAN"
## [11] "gi|547749|sp|P13645|K1C10_HUMAN" "sp|P18624|EF1A_DICDI"
## [13] "sp|P54657|CAD1_DICDI" "sp|Q9GRF8|EF1B_DICDI"
## [15] "sp|P36415|HS7C1_DICDI" "gi|547754|sp|P35908|K22E_HUMAN"
## [17] "sp|Q869W9|PKS16_DICDI" "sp|P15112|EF2_DICDI"
## [19] "sp|Q54VI4|GSTA3_DICDI" "sp|P42530|DIS2_DICDI"
## [21] "sp|P54651|HSC90_DICDI" "sp|Q94469|G3P_DICDI"
## [23] "sp|P05095|ACTNA_DICDI" "sp|Q54D04|MDHB_DICDI"
## [25] "sp|P54638|ARGE_DICDI" "sp|Q55CS9|ATPB_DICDI"
## [27] "sp|Q553V1|CISYM_DICDI" "sp|P10819|SAHH_DICDI"
## [29] "sp|P22887|NDKC_DICDI" "sp|Q557E0|HS7C2_DICDI"
## [31] "sp|Q54JC8|PUR4_DICDI" "sp|P20054|PYR1_DICDI"
## [33] "sp|P02887|DIS1B_DICDI" "sp|P13466|GELA_DICDI"
## [35] "sp|P02931|OMPF_ECOLI" "sp|Q54F07|METK_DICDI"
## [37] "sp|Q54RK5|ENOA_DICDI" "sp|P24639|ANXA7_DICDI"
## [39] "sp|Q75JR2|IDHP_DICDI" "sp|Q86A67|ALF_DICDI"
## [41] "sp|P54632|1433_DICDI" "sp|Q556J0|TKT_DICDI"
## [43] "sp|Q8I0H7|HSP7M_DICDI" "sp|Q54F93|MPPA2_DICDI"
## [45] "sp|P15064|RASG_DICDI" "sp|Q4W6B5|MPPB_DICDI"
## [47] "tr|Q54RZ4|Q54RZ4_DICDI" "sp|Q54VI3|GLUD2_DICDI"
## [49] "sp|Q55DV9|CGL_DICDI" "tr|P90529|P90529_DICDI"
## [51] "sp|Q557D2|G6PD_DICDI" "sp|Q55FF3|GSTA1_DICDI"
## [53] "sp|Q23858|CALR_DICDI" "sp|Q9XPJ9|ATPAM_DICDI"
## [55] "tr|P90532|P90532_DICDI" "sp|P54647|VATA_DICDI"
## [57] "sp|O97470|ADT_DICDI" "sp|P83401|AL7A1_DICDI"
## [59] "sp|O77229|CATA_DICDI" "sp|Q86L14|PUR9_DICDI"
## [61] "sp|Q54XS2|ACON_DICDI" "sp|Q54Z26|GLYC1_DICDI"
## [63] "tr|Q54P24|Q54P24_DICDI" "sp|Q86IA3|PDI1_DICDI"
## [65] "sp|Q55C16|UBA1_DICDI" "sp|P54659|MVPB_DICDI"
## [67] "sp|Q54HB2|EFTU_DICDI" "sp|Q54Z69|RL4_DICDI"
## [69] "tr|Q54F16|Q54F16_DICDI" "sp|O76856|CATD_DICDI"
## [71] "sp|Q76NU1|VATB_DICDI" "sp|Q54J47|PDX1_DICDI"
## [73] "sp|Q54X49|METE_DICDI" "sp|Q01501|VDAC_DICDI"
## [75] "sp|Q8T869|BIP2_DICDI" "sp|Q8TA03|6PGD_DICDI"
## [77] "sp|Q6TMK3|HSP88_DICDI" "sp|O00780|VATE_DICDI"
## [79] "sp|Q557H1|DPP3_DICDI" "tr|Q55CY7|Q55CY7_DICDI"
## [81] "sp|Q54EW8|DLDH_DICDI" "sp|Q9GPM4|PGK_DICDI"
## [83] "sp|Q75JD5|PCKA_DICDI" "sp|Q54YA0|ACLY_DICDI"
## [85] "sp|Q54I10|MMSA_DICDI" "sp|P34118|MVPA_DICDI"
## [87] "sp|Q54WR9|GLNA3_DICDI" "sp|Q54KB7|DHE3_DICDI"
## [89] "sp|P24005|ACTB_DICDI" "sp|P54654|CAP_DICDI"
## [91] "sp|Q76NV5|HPPD_DICDI" "sp|Q54BF6|MCFN_DICDI"
## [93] "sp|Q54RA2|AL4A1_DICDI" "sp|Q554S6|ACT17_DICDI"
## [95] "sp|Q54QQ0|IMDH_DICDI" "sp|P25870|CLH_DICDI"
## [97] "sp|Q54SF7|AATC_DICDI" "sp|Q75JR3|IDHC_DICDI"
## [99] "sp|P33519|RAN_DICDI" "tr|Q75JY8|Q75JY8_DICDI"
## [101] "sp|Q54ZW5|RS3A_DICDI" "sp|Q54JE4|ODO1_DICDI"
## [103] "sp|P36408|GBB_DICDI" "sp|Q9NKX1|ENPL_DICDI"
## [105] "tr|Q557C7|Q557C7_DICDI" "tr|Q86HU8|Q86HU8_DICDI"
## [107] "sp|Q552J0|TCPQ_DICDI" "sp|P27133|CORO_DICDI"
## [109] "sp|Q869S7|SUCB2_DICDI" "sp|O96621|ARP2_DICDI"
## [111] "sp|Q54X73|ACOC_DICDI" "sp|Q54EW2|Y9130_DICDI"
## [113] "sp|P22685|RLA0_DICDI" "tr|Q553T0|Q553T0_DICDI"
## [115] "sp|Q54I98|SMT1_DICDI" "tr|Q55CT4|Q55CT4_DICDI"
## [117] "sp|Q9NKW1|MFEA_DICDI" "sp|Q54K32|RGAA_DICDI"
## [119] "sp|P21900|PURA_DICDI" "sp|P34113|RL3_DICDI"
## [121] "tr|Q54BI8|Q54BI8_DICDI" "sp|Q55BY1|RSSA_DICDI"
## [123] "sp|Q869Y7|ODO2_DICDI" "sp|Q554D9|SYNC_DICDI"
## [125] "sp|Q54DF1|ATPG_DICDI" "sp|Q54YT4|MECR_DICDI"
## [127] "tr|Q86IY4|Q86IY4_DICDI" "sp|Q54LT2|URIC_DICDI"
## [129] "sp|Q54ZD1|RL7A_DICDI" "sp|P32256|TBB_DICDI"
## [131] "sp|Q55AI5|SUCB1_DICDI" "sp|Q55EQ3|Y9086_DICDI"
## [133] "tr|Q54L07|Q54L07_DICDI" "sp|Q54UU3|RS6_DICDI"
## [135] "sp|Q54JP5|OAT_DICDI" "tr|Q55FM7|Q55FM7_DICDI"
## [137] "sp|Q54KB8|SYEC_DICDI" "sp|P11874|RL7_DICDI"
## [139] "sp|Q54M18|ARG56_DICDI" "tr|Q54P64|Q54P64_DICDI"
## [141] "tr|Q54JD4|Q54JD4_DICDI" "sp|Q54MB5|ADK_DICDI"
## [143] "sp|P42528|ARP3_DICDI" "sp|P34093|NDKM_DICDI"
## [145] "sp|P54661|SMLA_DICDI" "tr|Q54X02|Q54X02_DICDI"
## [147] "tr|Q54WH0|Q54WH0_DICDI" "sp|Q54JM5|PSMD1_DICDI"
## [149] "tr|Q54Q30|Q54Q30_DICDI" "sp|Q54DK1|SQRD_DICDI"
## [151] "sp|Q54N83|SYLC_DICDI" "sp|Q54UH8|SERA_DICDI"
## [153] "sp|Q54Y20|SYA_DICDI" "sp|P0A6N1|EFTU_ECOLI"
## [155] "sp|Q54QE4|PURCE_DICDI" "tr|Q55GC0|Q55GC0_DICDI"
## [157] "sp|Q54VG4|SGT_DICDI" "tr|Q55EX9|Q55EX9_DICDI"
## [159] "sp|Q55GQ5|SODC1_DICDI" "sp|Q54G21|FKBP5_DICDI"
## [161] "sp|Q54BM2|PAP1A_DICDI" "sp|P36967|SUCA_DICDI"
## [163] "sp|P36411|RAB7A_DICDI" "sp|Q54BC6|PSMD2_DICDI"
## [165] "sp|P51405|RS4_DICDI" "tr|Q54PF9|Q54PF9_DICDI"
## [167] "sp|P11872|M3R_DICDI" "sp|P13231|HATA_DICDI"
## [169] "sp|Q54EN4|PDI2_DICDI" "sp|Q54E49|DDX6_DICDI"
## [171] "sp|Q54GT6|SPYA_DICDI" "tr|Q553S7|Q553S7_DICDI"
## [173] "sp|Q9U641|CMFB_DICDI" "tr|Q54TY4|Q54TY4_DICDI"
## [175] "sp|Q54E04|VATM_DICDI" "sp|Q55F21|AATM_DICDI"
## [177] "sp|Q54RA8|ATPO_DICDI" "sp|P46800|GBLP_DICDI"
## [179] "sp|Q00766|PHS1_DICDI" "sp|Q55FI1|GABT_DICDI"
## [181] "sp|Q54KD9|RANG_DICDI" "sp|Q54YD4|SYIC_DICDI"
## [183] "sp|Q54E24|RS8_DICDI" "sp|P46794|CBS_DICDI"
## [185] "sp|O96623|ARPC2_DICDI" "sp|Q55FT1|DPYD_DICDI"
## [187] "sp|Q54GJ2|PUR2_DICDI" "sp|Q5V9F0|AMPL_DICDI"
## [189] "sp|P14132|RS9_DICDI" "sp|Q54J66|SYTC1_DICDI"
## [191] "sp|Q54DD2|THOPL_DICDI" "sp|Q54D63|RL6_DICDI"
## [193] "sp|Q55DR6|FCSA_DICDI" "sp|P54641|VA0D_DICDI"
## [195] "sp|Q54XX3|RL5_DICDI" "sp|Q54DA8|STIP1_DICDI"
## [197] "sp|Q54J97|CH60_DICDI" "sp|Q54Q51|RS16_DICDI"
## [199] "sp|Q54V77|PYRG_DICDI" "sp|P09556|PYR5_DICDI"
## [201] "sp|O60952|LIME_DICDI" "tr|Q86AQ3|Q86AQ3_DICDI"
## [203] "sp|P90526|RS3_DICDI" "tr|O76187|O76187_DICDI"
## [205] "sp|P0A910|OMPA_ECOLI" "sp|Q54HG2|CTXA_DICDI"
## [207] "sp|P54648|VATC_DICDI" "tr|Q54DX3|Q54DX3_DICDI"
## [209] "sp|Q54KZ8|EIF3M_DICDI" "sp|P52922|LKHA4_DICDI"
## [211] "tr|Q55CR6|Q55CR6_DICDI" "sp|Q557J6|DBNL_DICDI"
## [213] "sp|Q1ZXG4|PRLA_DICDI" "sp|Q54D07|CY1_DICDI"
## [215] "sp|Q54VM2|MDHC_DICDI" "sp|Q86KD1|CAND1_DICDI"
## [217] "tr|Q75K16|Q75K16_DICDI" "sp|Q86KU2|SYVC_DICDI"
## [219] "sp|Q9UAG7|FHBA_DICDI" "tr|Q54PA2|Q54PA2_DICDI"
## [221] "sp|Q9U3X4|DHSA_DICDI" "sp|Q95VZ3|CARML_DICDI"
## [223] "sp|P32073|GUAA_DICDI" "tr|Q86K89|Q86K89_DICDI"
## [225] "sp|Q6IMN8|ALRA_DICDI" "sp|Q54C70|ODPA_DICDI"
## [227] "tr|Q54XR4|Q54XR4_DICDI" "sp|P10733|SEVE_DICDI"
## [229] "tr|Q54G77|Q54G77_DICDI" "sp|Q75JI3|NSF_DICDI"
## [231] "sp|P16168|RL11_DICDI" "tr|Q54FH4|Q54FH4_DICDI"
## [233] "sp|Q55CT5|SEC31_DICDI" "sp|Q86L05|RL10A_DICDI"
## [235] "sp|Q964D8|BUP1_DICDI" "tr|Q54N76|Q54N76_DICDI"
## [237] "sp|P14329|RL19_DICDI" "sp|Q54E20|RL13_DICDI"
## [239] "sp|Q86K01|RL15_DICDI" "sp|Q54QS3|DDX3_DICDI"
## [241] "tr|Q54KZ1|Q54KZ1_DICDI" "sp|Q54J69|RL10_DICDI"
## [243] "sp|Q555Q9|GLGB_DICDI" "sp|Q54PV8|IPYR_DICDI"
## [245] "sp|Q54UP4|TALDO_DICDI" "sp|Q54YZ0|UGPA2_DICDI"
## [247] "sp|P34144|RAC1A_DICDI" "sp|P20790|RAB8A_DICDI"
## [249] "tr|Q556G1|Q556G1_DICDI" "sp|Q86AS5|PPCE_DICDI"
## [251] "sp|Q54J34|PUR8_DICDI" "sp|P36410|RAB14_DICDI"
## [253] "sp|P27685|RS2_DICDI" "tr|Q55AN3|Q55AN3_DICDI"
## [255] "sp|P54680|FIMB_DICDI" "sp|Q54UF7|LDHD_DICDI"
## [257] "sp|O96622|ARPC1_DICDI" "sp|Q55E60|PEPD_DICDI"
## [259] "tr|Q55F70|Q55F70_DICDI" "sp|Q54BQ3|RL23A_DICDI"
## [261] "sp|P14325|SYQ_DICDI" "sp|P13022|CAPZA_DICDI"
## [263] "tr|Q54U95|Q54U95_DICDI" "sp|P13651|IF5A_DICDI"
## [265] "sp|Q54JD9|SCOT_DICDI" "sp|Q54VG0|DHTK1_DICDI"
## [267] "sp|P34098|MANA_DICDI" "tr|Q54WL7|Q54WL7_DICDI"
## [269] "tr|Q54NV9|Q54NV9_DICDI" "sp|P08799|MYS2_DICDI"
## [271] "tr|Q7KWR1|Q7KWR1_DICDI" "sp|Q54UJ0|PSD12_DICDI"
## [273] "sp|Q54D73|FHBB_DICDI" "sp|Q54PA9|PRPSA_DICDI"
## [275] "sp|Q54C24|VPS35_DICDI" "sp|P36413|ODP2_DICDI"
## [277] "tr|Q8T131|Q8T131_DICDI" "sp|Q55BZ4|PCY2_DICDI"
## [279] "sp|P54686|WD42_DICDI" "sp|Q86IV4|Y4775_DICDI"
## [281] "sp|Q54IT3|AOFA_DICDI" "sp|P60422|RL2_ECOLI"
## [283] "sp|P15808|THY1_DICDI" "sp|P18613|RAPA_DICDI"
## [285] "sp|Q54QJ9|KAD2_DICDI" "tr|Q54QK0|Q54QK0_DICDI"
## [287] "sp|Q75JQ1|SYDC1_DICDI" "sp|Q55C75|PSMD6_DICDI"
## [289] "sp|P90521|K6PF_DICDI" "tr|Q54TD0|Q54TD0_DICDI"
## [291] "sp|Q5XM24|APRA_DICDI" "sp|Q55BI2|IDHA_DICDI"
## [293] "sp|Q869Q3|NAPA_DICDI" "tr|Q54GL5|Q54GL5_DICDI"
## [295] "tr|Q86JG3|Q86JG3_DICDI" "sp|Q54WI8|PSD7_DICDI"
## [297] "sp|Q8T2K9|MASY_DICDI" "sp|Q86KU6|MTD1_DICDI"
## [299] "tr|Q55ET7|Q55ET7_DICDI" "sp|Q54QX3|CSN1_DICDI"
## [301] "sp|Q86AD9|THIL1_DICDI" "sp|Q54SM3|PPIA_DICDI"
## [303] "sp|Q54HL0|COPG_DICDI" "sp|Q54RF5|KPYK_DICDI"
## [305] "tr|Q54T96|Q54T96_DICDI" "sp|Q54P71|Y4755_DICDI"
## [307] "sp|Q1ZXD3|PSMD3_DICDI" "tr|Q54M03|Q54M03_DICDI"
## [309] "sp|Q54KM7|GCSP_DICDI" "sp|Q54VA2|FUMH_DICDI"
## [311] "tr|Q55FM2|Q55FM2_DICDI" "tr|Q75JL7|Q75JL7_DICDI"
## [313] "sp|Q23919|PGM1_DICDI" "sp|B0M0P5|DDB1_DICDI"
## [315] "tr|Q75JW5|Q75JW5_DICDI" "tr|Q54T94|Q54T94_DICDI"
## [317] "sp|Q54NG2|RL17_DICDI" "tr|Q54QS2|Q54QS2_DICDI"
## [319] "tr|Q54JZ3|Q54JZ3_DICDI" "tr|Q54D66|Q54D66_DICDI"
## [321] "sp|Q55EK2|C524A_DICDI" "sp|Q54Z09|RL14_DICDI"
## [323] "tr|Q54M58|Q54M58_DICDI" "sp|Q54X95|SYMC_DICDI"
## [325] "sp|Q54SE2|PRDXL_DICDI" "sp|Q550D2|SYFB_DICDI"
## [327] "sp|Q54EW3|IPO5_DICDI" "tr|Q55D63|Q55D63_DICDI"
## [329] "tr|Q54C34|Q54C34_DICDI" "sp|P25323|MYLKA_DICDI"
## [331] "tr|Q54JA0|Q54JA0_DICDI" "tr|Q55CJ9|Q55CJ9_DICDI"
## [333] "sp|Q54I90|NDUV1_DICDI" "sp|Q54PX9|RS12_DICDI"
## [335] "sp|Q55CA0|VPS26_DICDI" "sp|Q9NGQ2|KIF1_DICDI"
## [337] "sp|Q86HX0|ODPB_DICDI" "tr|Q54HZ2|Q54HZ2_DICDI"
## [339] "sp|Q54Q48|Y5844_DICDI" "sp|Q54NB6|FKBP4_DICDI"
## [341] "sp|P02886|DIS1A_DICDI" "sp|P36412|RB11A_DICDI"
## [343] "tr|Q54FU0|Q54FU0_DICDI" "sp|Q65YR8|CYTA1_DICDI"
## [345] "sp|Q94504|CYSP7_DICDI" "sp|Q54GE6|MDHA_DICDI"
## [347] "sp|Q8T2H0|FAM49_DICDI" "sp|Q54B67|MCFZ_DICDI"
## [349] "tr|Q54Z72|Q54Z72_DICDI" "sp|Q553B6|NMT_DICDI"
## [351] "sp|O00909|ARF1_DICDI" "sp|Q55DB4|T23O_DICDI"
## [353] "sp|Q54EG3|RS14_DICDI" "sp|Q54NP6|SNAA_DICDI"
## [355] "sp|Q559Z0|NDUA9_DICDI" "sp|Q86A72|Y4781_DICDI"
## [357] "sp|Q54K67|MANG_DICDI" "sp|Q54PJ1|PRS10_DICDI"
## [359] "sp|Q54C27|OST1_DICDI" "sp|Q54NE6|PGAM_DICDI"
## [361] "tr|Q54F09|Q54F09_DICDI" "sp|Q34312|NDUS1_DICDI"
## [363] "sp|P54653|CBP2_DICDI" "sp|Q54M70|DNPEP_DICDI"
## [365] "tr|Q7KWS3|Q7KWS3_DICDI" "tr|Q54VB8|Q54VB8_DICDI"
## [367] "tr|Q86K67|Q86K67_DICDI" "sp|Q54VZ4|RL18_DICDI"
## [369] "sp|P0A7L0|RL1_ECOLI" "sp|O77082|RS10_DICDI"
## [371] "tr|Q75JT6|Q75JT6_DICDI" "sp|P0A6M8|EFG_ECOLI"
## [373] "tr|Q54FT2|Q54FT2_DICDI" "sp|Q5V9E9|G6PI_DICDI"
## [375] "sp|Q553M7|RL13A_DICDI" "sp|O96625|ARPC4_DICDI"
## [377] "sp|Q54HG9|RPN2_DICDI" "sp|Q54GQ6|ACOX1_DICDI"
## [379] "tr|Q54W88|Q54W88_DICDI" "tr|Q86KZ6|Q86KZ6_DICDI"
## [381] "sp|Q9GS21|RL28_DICDI" "sp|Q55F34|DPNP_DICDI"
## [383] "sp|P63284|CLPB_ECOLI" "sp|Q1ZXQ1|FAAA_DICDI"
## [385] "sp|Q54NQ0|PSD13_DICDI" "tr|Q55GT3|Q55GT3_DICDI"
## [387] "sp|Q54BC8|PSB5_DICDI" "sp|Q54NJ8|HPRT_DICDI"
## [389] "sp|Q54XF7|CUL5_DICDI" "sp|Q559R0|SAR1A_DICDI"
## [391] "sp|Q54CX6|3HIDH_DICDI" "sp|Q9NCL8|PITP1_DICDI"
## [393] "sp|P13021|CAPZB_DICDI" "sp|Q54UU8|TRXB_DICDI"
## [395] "sp|Q54KS8|SYFA_DICDI" "sp|Q54MA6|RS5_DICDI"
## [397] "sp|Q9TW32|PPIB_DICDI" "sp|Q7YZN9|ERF3_DICDI"
## [399] "sp|Q556U6|BIP1_DICDI" "tr|Q54IU0|Q54IU0_DICDI"
## [401] "tr|Q54S30|Q54S30_DICDI" "sp|Q1ZXH9|CP51_DICDI"
## [403] "sp|Q869T1|TWF_DICDI" "sp|Q54N47|BCAT_DICDI"
## [405] "sp|Q54X53|RL21_DICDI" "tr|Q54TS5|Q54TS5_DICDI"
## [407] "sp|Q54X97|EIF3C_DICDI" "sp|Q55BE6|RL27_DICDI"
## [409] "sp|Q869Z4|PCKGM_DICDI" "tr|Q86IB4|Q86IB4_DICDI"
## [411] "sp|Q94502|GANAB_DICDI" "tr|Q54II1|Q54II1_DICDI"
## [413] "tr|Q54F90|Q54F90_DICDI" "sp|Q869N9|IF2A_DICDI"
## [415] "tr|Q54UM3|Q54UM3_DICDI" "tr|O96455|O96455_DICDI (+1)"
## [417] "sp|Q7YXU4|CPNA_DICDI" "sp|Q54RX6|TCTP1_DICDI"
## [419] "sp|Q54XD8|IF2G_DICDI" "tr|Q54E73|Q54E73_DICDI"
## [421] "sp|P0AG55|RL6_ECOLI" "tr|Q54JS9|Q54JS9_DICDI"
## [423] "sp|Q54TR8|NACB_DICDI" "sp|Q55E54|COROB_DICDI"
## [425] "sp|Q54MJ7|ALAM_DICDI" "sp|Q23862|RACE_DICDI"
## [427] "sp|P48160|RL27A_DICDI" "sp|P16894|GPA1_DICDI"
## [429] "tr|Q76P15|Q76P15_DICDI" "tr|Q8T2R4|Q8T2R4_DICDI"
## [431] "sp|P13023|RL8_DICDI" "sp|Q54XS1|PH4H_DICDI"
## [433] "sp|Q54KR1|SYCC_DICDI" "sp|P03023|LACI_ECOLI"
## [435] "tr|Q55F80|Q55F80_DICDI" "sp|Q54KE6|MCCA_DICDI"
## [437] "sp|Q54JL3|FTCD_DICDI" "tr|Q54G53|Q54G53_DICDI"
## [439] "tr|Q869X1|Q869X1_DICDI" "sp|Q54I41|RS7_DICDI"
## [441] "sp|Q54MK8|RL18A_DICDI" "sp|Q54B68|IDHB_DICDI"
## [443] "tr|Q54DD1|Q54DD1_DICDI" "sp|Q86AS6|SYHC_DICDI"
## [445] "sp|Q54LV3|EFTS_DICDI" "sp|Q55ED1|NAP1_DICDI"
## [447] "sp|Q55BJ9|SODM_DICDI" "sp|P0A9B2|G3P1_ECOLI"
## [449] "tr|Q54PL1|Q54PL1_DICDI" "sp|Q54S90|RS11_DICDI"
## [451] "sp|Q54EW1|GLYC2_DICDI" "sp|Q55G04|PSA5_DICDI"
## [453] "tr|Q54CA6|Q54CA6_DICDI" "sp|P60723|RL4_ECOLI"
## [455] "tr|Q54UV0|Q54UV0_DICDI" "sp|Q54RB9|CMC_DICDI"
## [457] "sp|Q54N17|RS15_DICDI" "sp|Q76P23|PM34_DICDI"
## [459] "sp|Q54T59|SEC23_DICDI" "tr|Q54E45|Q54E45_DICDI"
## [461] "sp|O96624|ARPC3_DICDI" "tr|Q54HR9|Q54HR9_DICDI"
## [463] "sp|Q54TL8|RS26_DICDI" "sp|Q54PM7|NAGK_DICDI"
## [465] "tr|Q86IF6|Q86IF6_DICDI" "tr|Q54JS1|Q54JS1_DICDI"
## [467] "tr|Q8MMS1|Q8MMS1_DICDI" "sp|Q54QM8|RL26_DICDI"
## [469] "sp|Q94465|GCH1_DICDI" "tr|Q54RW1|Q54RW1_DICDI"
## [471] "sp|Q869U7|RS18_DICDI" "tr|Q551V0|Q551V0_DICDI"
## [473] "sp|Q54Y41|RS20_DICDI" "sp|Q55FK4|APT12_DICDI"
## [475] "sp|Q54U07|NACA_DICDI" "sp|O96759|ADAS_DICDI"
## [477] "sp|Q54NC1|NCB5R_DICDI" "tr|Q55BV1|Q55BV1_DICDI"
## [479] "tr|Q869Z5|Q869Z5_DICDI" "sp|P0A7R1|RL9_ECOLI"
## [481] "sp|Q54X03|PMM1_DICDI" "sp|Q54FD7|ETFA_DICDI"
## [483] "sp|Q86A21|PSB1_DICDI" "sp|Q54XI5|RL9_DICDI"
## [485] "sp|Q54YN2|MAAI_DICDI" "sp|Q54QR3|RB32A_DICDI"
## [487] "sp|Q54QR2|PSB7_DICDI" "sp|Q55E06|HEM2_DICDI"
## [489] "tr|Q9BI24|Q9BI24_DICDI" "sp|Q54MB4|ASNS_DICDI"
## [491] "sp|Q556N5|ADRM1_DICDI" "sp|Q54RE8|KMO_DICDI"
## [493] "tr|Q54R90|Q54R90_DICDI" "tr|Q869N7|Q869N7_DICDI"
## [495] "sp|Q54XC1|5NTC_DICDI" "tr|Q54DZ8|Q54DZ8_DICDI"
## [497] "sp|Q75K27|RS24_DICDI" "sp|Q54MR1|GLO2_DICDI"
## [499] "tr|Q54F74|Q54F74_DICDI" "sp|Q54G86|RL23_DICDI"
## [501] "tr|Q54XI2|Q54XI2_DICDI (+1)" "sp|Q54XK2|SC61A_DICDI"
## [503] "sp|Q86A77|VATD_DICDI" "sp|Q5TJ65|VASP_DICDI"
## [505] "sp|Q54YD8|COPB2_DICDI" "sp|Q86A17|DHPR_DICDI"
## [507] "sp|Q55CQ6|SERC_DICDI" "sp|Q86KI1|AP2A2_DICDI"
## [509] "sp|Q54JD2|Y8137_DICDI" "sp|Q54RR5|ACDSB_DICDI"
## [511] "sp|Q869R8|TPIS_DICDI" "sp|Q54K91|ACH1_DICDI"
## [513] "sp|Q54QW1|EIF3B_DICDI" "sp|P61889|MDH_ECOLI"
## [515] "tr|Q869L6|Q869L6_DICDI" "sp|Q54CJ3|6PGL_DICDI"
## [517] "sp|P0A7J3|RL10_ECOLI" "sp|Q55DY7|PSB2_DICDI"
## [519] "sp|Q54XM7|PSA6_DICDI" "sp|Q559X6|RAB2B_DICDI"
## [521] "tr|Q86I62|Q86I62_DICDI" "sp|Q27562|PSA1_DICDI"
## [523] "tr|Q9NH03|Q9NH03_DICDI" "sp|Q54BM7|PPK_DICDI"
## [525] "sp|P34119|PSA4_DICDI" "tr|Q54Q74|Q54Q74_DICDI"
## [527] "sp|P42520|RS17_DICDI" "sp|Q55C99|SYDM_DICDI"
## [529] "sp|Q54X51|RS19_DICDI" "tr|Q86J04|Q86J04_DICDI"
## [531] "sp|Q54RX9|RAB5B_DICDI" "sp|P06959|ODP2_ECOLI"
## [533] "tr|Q54PW3|Q54PW3_DICDI" "tr|Q555X7|Q555X7_DICDI"
## [535] "sp|Q6TU48|MAOX_DICDI" "sp|Q1ZXF1|ECHM_DICDI"
## [537] "tr|Q54CW1|Q54CW1_DICDI" "tr|Q75JE7|Q75JE7_DICDI"
## [539] "sp|Q86AD5|Y1564_DICDI" "sp|P34136|HMDH2_DICDI"
## [541] "sp|Q8T1V6|NDUS4_DICDI" "sp|Q1ZXQ4|FCSB_DICDI"
## [543] "sp|Q8T137|GSHR_DICDI" "sp|P34142|RAB21_DICDI"
## [545] "sp|Q55C77|FCL_DICDI" "sp|Q54NZ7|ALRB_DICDI"
## [547] "sp|Q1ZXF7|GMDS_DICDI" "sp|Q54PF3|CSN5_DICDI"
## [549] "sp|O15706|VACA_DICDI" "tr|Q55D50|Q55D50_DICDI"
## [551] "sp|Q54JQ2|IDE_DICDI" "sp|P34120|PSA7_DICDI"
## [553] "sp|Q54K39|GMPPB_DICDI" "sp|P0A6Z3|HTPG_ECOLI"
## [555] "sp|Q55GJ6|PSB6_DICDI" "sp|P54201|UBPA_DICDI"
## [557] "sp|Q55D66|PSB3_DICDI" "sp|Q54IF7|VPS29_DICDI"
## [559] "sp|Q54EQ1|EIF3K_DICDI" "sp|Q54NU2|RAB1D_DICDI"
## [561] "tr|O15744|O15744_DICDI" "sp|Q86IJ1|PSDE_DICDI"
## [563] "tr|Q54QE7|Q54QE7_DICDI" "sp|Q55FK2|RAB6_DICDI"
## [565] "sp|Q55G10|PNCB_DICDI" "tr|Q54P88|Q54P88_DICDI"
## [567] "sp|Q9U9A3|PPP6_DICDI" "sp|Q54SV3|ALN1_DICDI"
## [569] "sp|Q54ML1|NADE_DICDI" "tr|Q54HG8|Q54HG8_DICDI"
## [571] "sp|P0AG67|RS1_ECOLI" "sp|Q54DG1|ALDH3_DICDI"
## [573] "sp|Q54VG1|GPA12_DICDI" "sp|Q54QG9|UBA3_DICDI"
## [575] "tr|Q54T87|Q54T87_DICDI" "sp|P02413|RL15_ECOLI"
## [577] "tr|Q86I47|Q86I47_DICDI" "sp|Q54YZ4|ETFB_DICDI"
## [579] "sp|P02359|RS7_ECOLI" "sp|Q8T2J9|MCCB_DICDI"
## [581] "sp|P32253|RASC_DICDI" "tr|Q54N71|Q54N71_DICDI"
## [583] "sp|P0A9Q7|ADHE_ECOLI" "tr|Q75JS7|Q75JS7_DICDI"
## [585] "sp|Q54J23|RL35_DICDI" "sp|Q54DM7|PSA2_DICDI"
## [587] "tr|Q54FV6|Q54FV6_DICDI" "tr|Q86HW2|Q86HW2_DICDI"
## [589] "sp|P34139|RAB1A_DICDI" "sp|Q9Y0B7|PP4C_DICDI"
## [591] "tr|Q54FY2|Q54FY2_DICDI" "sp|Q54F10|NDUV2_DICDI"
## [593] "tr|Q54MF0|Q54MF0_DICDI" "sp|Q54DY1|ARGJ_DICDI"
## [595] "sp|P32254|RASS_DICDI" "sp|Q55FU2|COPE_DICDI"
## [597] "sp|P0C7B6|GLNA2_DICDI (+1)" "sp|Q54MP2|CCD22_DICDI"
## [599] "sp|Q75JX0|NADC_DICDI" "sp|Q54CC5|EIF3E_DICDI"
## [601] "sp|Q54PH8|RS13_DICDI" "tr|Q55D72|Q55D72_DICDI"
## [603] "sp|Q54B82|CSN4_DICDI" "tr|Q55GC5|Q55GC5_DICDI"
## [605] "tr|Q54IU1|Q54IU1_DICDI" "tr|Q54GQ7|Q54GQ7_DICDI"
## [607] "sp|Q54DD0|AMPD_DICDI" "sp|Q54II8|U553_DICDI"
## [609] "tr|Q54T47|Q54T47_DICDI" "sp|Q54C92|CSN6_DICDI"
## [611] "sp|P32252|RASB_DICDI" "sp|P0A799|PGK_ECOLI"
## [613] "tr|Q86JB5|Q86JB5_DICDI" "tr|Q8T849|Q8T849_DICDI"
## [615] "sp|Q54IK1|RRAGA_DICDI" "tr|Q54ES4|Q54ES4_DICDI"
## [617] "sp|Q55AB5|RL32_DICDI" "tr|Q54U01|Q54U01_DICDI"
## [619] "sp|Q55C72|U587_DICDI" "sp|Q55C21|AMPM2_DICDI"
## [621] "tr|Q54VU3|Q54VU3_DICDI" "sp|Q55A19|RS23_DICDI"
## [623] "tr|Q54C69|Q54C69_DICDI" "sp|Q86KZ5|SBDS_DICDI"
## [625] "sp|Q869N6|CRTL_DICDI" "tr|Q54SQ0|Q54SQ0_DICDI"
## [627] "sp|Q9XZE5|PP2AA_DICDI" "sp|Q54BM3|MCFG_DICDI"
## [629] "sp|Q54EJ5|GLOB2_DICDI" "sp|Q55E13|ZPR1_DICDI"
## [631] "tr|Q54C73|Q54C73_DICDI" "sp|P0ABK5|CYSK_ECOLI"
## [633] "tr|Q54G88|Q54G88_DICDI" "sp|Q54J50|RL12_DICDI"
## [635] "sp|Q75K24|CSN8_DICDI" "tr|Q54W38|Q54W38_DICDI"
## [637] "tr|Q54WP6|Q54WP6_DICDI" "tr|Q54BX7|Q54BX7_DICDI"
## [639] "sp|P54706|COF1_DICDI" "sp|P34149|RACC_DICDI"
## [641] "tr|Q58A42|Q58A42_DICDI" "sp|P0AFG8|ODP1_ECOLI"
## [643] "tr|Q55EJ0|Q55EJ0_DICDI" "sp|Q556Q0|PSB4_DICDI"
## [645] "tr|Q54JU2|Q54JU2_DICDI" "sp|Q54D08|LST8_DICDI"
## [647] "sp|Q54IM8|ACAD8_DICDI" "sp|Q55FN7|ODBB_DICDI"
## [649] "tr|Q54QC5|Q54QC5_DICDI" "tr|B0G197|B0G197_DICDI"
## [651] "sp|O96626|ARPC5_DICDI" "sp|P0A7Z4|RPOA_ECOLI"
## [653] "tr|Q54IW4|Q54IW4_DICDI" "sp|Q54DP1|TKRA_DICDI"
## [655] "sp|Q54XZ0|MFEB_DICDI" "sp|Q54FL2|RABG2_DICDI"
## [657] "tr|Q54I22|Q54I22_DICDI" "sp|Q54S59|WDR61_DICDI"
## [659] "sp|Q55GQ6|UPP_DICDI" "sp|Q27563|PSA3_DICDI"
## [661] "tr|Q8T8P2|Q8T8P2_DICDI" "sp|Q54P23|SDF2_DICDI"
## [663] "tr|Q86L27|Q86L27_DICDI" "sp|Q54XM6|ETFD_DICDI"
## [665] "tr|Q551A9|Q551A9_DICDI" "tr|Q54G31|Q54G31_DICDI"
## [667] "sp|Q8MQU6|CISYP_DICDI" "sp|Q54DA9|HACL1_DICDI"
## [669] "sp|Q1ZXE8|SRP68_DICDI" "sp|Q54GP3|SPRE_DICDI"
## [671] "sp|Q54MT0|EIF3I_DICDI" "sp|Q54XH6|PHYD1_DICDI"
## [673] "sp|P36409|RAB2A_DICDI" "tr|Q55BS7|Q55BS7_DICDI"
## [675] "tr|Q54RB5|Q54RB5_DICDI" "tr|Q54CK4|Q54CK4_DICDI"
## [677] "tr|Q55CE9|Q55CE9_DICDI" "sp|Q86H45|ELP2_DICDI"
## [679] "sp|Q54LV8|RL34_DICDI" "tr|Q55E92|Q55E92_DICDI"
## [681] "sp|Q86K21|CPNB_DICDI" "sp|Q95YL5|PEFA_DICDI"
## [683] "sp|Q86K94|TPPC3_DICDI" "tr|Q54J53|Q54J53_DICDI"
## [685] "sp|Q54G70|SODC5_DICDI" "tr|Q54N53|Q54N53_DICDI"
## [687] "sp|P0A6P1|EFTS_ECOLI" "sp|Q54YJ6|IF1A_DICDI"
# use discern to get set difference
# use discern discern(c(a,b), c(c,d)) -> first takes union, then difference
Heatmap for uniprot presence/absence over bait, condition or experiment. Filtered on protein
filterd <- filter(all_data_dicty, uniprot == 'sp|P0A9K9|SLYD_ECOLI')
condition <- split(filterd$uniprot, all_data_dicty$condition)
## Warning in split.default(filterd$uniprot, all_data_dicty$condition): data length
## is not a multiple of split variable
bait <- split(filterd$uniprot, all_data_dicty$bait)
## Warning in split.default(filterd$uniprot, all_data_dicty$bait): data length is
## not a multiple of split variable
toy_condition_filtered = Venn(condition)
setmap(toy_condition_filtered, element_clustering = FALSE, set_clustering = FALSE)
## Warning in vegan::vegdist(t(df), method = "jaccard"): you have empty rows: their
## dissimilarities may be meaningless in method "jaccard"
## Warning in vegan::vegdist(t(df), method = "jaccard"): missing values in results
toy_bait_filtered = Venn(bait)
setmap(toy_bait_filtered, element_clustering = FALSE, set_clustering = FALSE)
## Warning in vegan::vegdist(t(df), method = "jaccard"): you have empty rows: their
## dissimilarities may be meaningless in method "jaccard"
## Warning in vegan::vegdist(t(df), method = "jaccard"): missing values in results
my_data <-all_data_dicty %>%
group_by(bait, condition) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = "_")
count(my_data, bait_condition, uniprot) %>%
spread(bait_condition, n, fill = 0) %>%
select(-uniprot) %>%
as.matrix() %>%
crossprod()
## Galpha2_starv Galpha2_veg Galpha4_Normal Galpha8_GDP
## Galpha2_starv 1603 1603 1603 1059
## Galpha2_veg 1603 1603 1603 1059
## Galpha4_Normal 1603 1603 1603 1059
## Galpha8_GDP 1059 1059 1059 1436
## Galpha8_GppNHp 1059 1059 1059 1436
## Galpha8_Normal 957 957 957 1218
## Gbeta1_Normal 736 736 736 739
## Gbeta2_Normal 736 736 736 739
## Rac1_Normal 1098 1098 1098 1131
## RapA_Normal 0 0 0 0
## RasB_Normal 609 609 609 567
## RasC_Normal 759 759 759 649
## RasG1_Normal 279 279 279 247
## Ric8_Normal 1098 1098 1098 1131
## Roco4_BACKGROUND 1098 1098 1098 1131
## Galpha8_GppNHp Galpha8_Normal Gbeta1_Normal Gbeta2_Normal
## Galpha2_starv 1059 957 736 736
## Galpha2_veg 1059 957 736 736
## Galpha4_Normal 1059 957 736 736
## Galpha8_GDP 1436 1218 739 739
## Galpha8_GppNHp 1436 1218 739 739
## Galpha8_Normal 1218 1228 692 692
## Gbeta1_Normal 739 692 876 876
## Gbeta2_Normal 739 692 876 876
## Rac1_Normal 1131 1015 749 749
## RapA_Normal 0 0 0 0
## RasB_Normal 567 515 443 443
## RasC_Normal 649 586 526 526
## RasG1_Normal 247 220 217 217
## Ric8_Normal 1131 1015 749 749
## Roco4_BACKGROUND 1131 1015 749 749
## Rac1_Normal RapA_Normal RasB_Normal RasC_Normal RasG1_Normal
## Galpha2_starv 1098 0 609 759 279
## Galpha2_veg 1098 0 609 759 279
## Galpha4_Normal 1098 0 609 759 279
## Galpha8_GDP 1131 0 567 649 247
## Galpha8_GppNHp 1131 0 567 649 247
## Galpha8_Normal 1015 0 515 586 220
## Gbeta1_Normal 749 0 443 526 217
## Gbeta2_Normal 749 0 443 526 217
## Rac1_Normal 1448 0 577 679 264
## RapA_Normal 0 461 0 0 0
## RasB_Normal 577 0 808 570 241
## RasC_Normal 679 0 570 998 300
## RasG1_Normal 264 0 241 300 337
## Ric8_Normal 1448 0 577 679 264
## Roco4_BACKGROUND 1448 0 577 679 264
## Ric8_Normal Roco4_BACKGROUND
## Galpha2_starv 1098 1098
## Galpha2_veg 1098 1098
## Galpha4_Normal 1098 1098
## Galpha8_GDP 1131 1131
## Galpha8_GppNHp 1131 1131
## Galpha8_Normal 1015 1015
## Gbeta1_Normal 749 749
## Gbeta2_Normal 749 749
## Rac1_Normal 1448 1448
## RapA_Normal 0 0
## RasB_Normal 577 577
## RasC_Normal 679 679
## RasG1_Normal 264 264
## Ric8_Normal 1448 1448
## Roco4_BACKGROUND 1448 1448
# check value from crossproduct
# Galpha2_starv
#RasG1_Normal 279
bait_condition <- split(my_data$uniprot, my_data$bait_condition)
toy = Venn(bait_condition)
length(overlap(toy, c("Galpha2_starv", "RasG1_Normal")))
## [1] 279
# same number of proteins reported
my_data <- all_data_dicty %>%
group_by(bait, condition) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = "_")
overlapping <- count(my_data, bait_condition, uniprot) %>%
spread(bait_condition, n, fill = 0) %>%
select(-uniprot) %>%
as.matrix() %>%
crossprod()
heatmap(overlapping)
overlap_long <- reshape2::melt(overlapping)
ggplot(overlap_long, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
my_data <- all_data_dicty %>%
group_by(bait, condition) %>%
tidyr::unite(bait_condition, c(bait, condition), sep = "_")
pivoted <- my_data %>%
select(uniprot, bait_condition, spectral_count) %>%
group_by(bait_condition) %>%
pivot_wider(names_from = uniprot, values_from = spectral_count, values_fill = 0) %>%
summarise_all(funs(sum)) %>%
tibble::column_to_rownames(var = "bait_condition")
## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
wdbc.pr <- prcomp(pivoted)
summary(wdbc.pr)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 1971.9458 1290.9757 838.30393 756.25286 636.05050
## Proportion of Variance 0.4743 0.2033 0.08572 0.06976 0.04935
## Cumulative Proportion 0.4743 0.6776 0.76331 0.83307 0.88242
## PC6 PC7 PC8 PC9 PC10
## Standard deviation 595.58323 440.54269 371.26426 355.02771 265.38000
## Proportion of Variance 0.04327 0.02367 0.01681 0.01537 0.00859
## Cumulative Proportion 0.92569 0.94936 0.96617 0.98154 0.99013
## PC11 PC12 PC13 PC14 PC15
## Standard deviation 204.91780 171.32312 93.43430 28.3651 5.069e-12
## Proportion of Variance 0.00512 0.00358 0.00106 0.0001 0.000e+00
## Cumulative Proportion 0.99526 0.99884 0.99990 1.0000 1.000e+00
screeplot(wdbc.pr, type = "l", npcs = 15, main = "Screeplot of the first 15 PCs")
abline(h = 1, col="red", lty=5)
legend("topright", legend=c("Eigenvalue = 1"),
col=c("red"), lty=5, cex=0.6)
cumpro <- cumsum(wdbc.pr$sdev^2 / sum(wdbc.pr$sdev^2))
plot(cumpro[0:15], xlab = "PC #", ylab = "Amount of explained variance", main = "Cumulative variance plot")
#abline(v = 6, col="blue", lty=5)
#abline(h = 0.88759, col="blue", lty=5)
#legend("topleft", legend=c("Cut-off @ PC6"),col=c("blue"), lty=5, cex=0.6)
plot(wdbc.pr$x[,1],wdbc.pr$x[,2], xlab="PC1 (47.4%)", ylab = "PC2 (20.3%)", main = "PC1 / PC2 - plot")
library("factoextra")
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
fviz_pca_ind(wdbc.pr, geom.ind = "point", pointshape = 21,
pointsize = 2,
fill.ind = rownames(pivoted),
col.ind = "black",
palette = "ucscgb",
# addEllipses = TRUE,
label = "var",
col.var = "black",
repel = TRUE,
legend.title = "bait_condition") +
ggtitle("2D PCA-plot") +
theme(plot.title = element_text(hjust = 0.5))
library(igraph)
##
## Attaching package: 'igraph'
## The following object is masked from 'package:tidyr':
##
## crossing
## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
library(visNetwork)
my_data <-all_data_dicty %>% group_by(bait, condition) %>% tidyr::unite(bait_condition, c(bait, condition), sep = "_")
x <- my_data %>% separate(uniprot, c("a", "b", "c", "protid", "genid", "source"), extra = "merge", fill = "left", sep = '[_|]')
x <- x %>% select(-one_of(c("a", "b", "c")))
# resulting in 17k+ interactions, hairball!, need to filter
new_x <- x %>% filter(spectral_count > 200)
nodes <- as.data.frame(unique(new_x$bait_condition))
nodes$shape = 'triangle'
colnames(nodes) <- c('id', 'shape')
nodes <- nodes %>% add_row(id = unique(new_x$genid), shape = 'circle')
#nodes <- nodes %>% add_row(id = unique(new_x$genid))
nodes$label <- nodes$id
# nodes (shapes) and edges (thickness = width, use normalized counts) need to be done
# nodes scale (value) on basis of number of interactions
# gene ids, are not complete, <NA> or missing. Check the seperate function!
my_edges <- data.frame(new_x$bait_condition, new_x$genid, new_x$z_score_spectral)
colnames(my_edges) <- c('from', 'to', 'value')
graph <- graph_from_data_frame(my_edges, directed = FALSE)
## Warning in graph_from_data_frame(my_edges, directed = FALSE): In `d' `NA'
## elements were replaced with string "NA"
# color groups based on community detection, method louvain
#cluster <- cluster_louvain(graph)
#cluster_df <- data.frame(as.list(membership(cluster)))
#cluster_df <- as.data.frame(t(cluster_df))
#cluster_df$label <- rownames(cluster_df)
#nodes <- left_join(nodes, cluster_df, by = 'label')
#colnames(nodes)[3] <- 'group'
visNetwork(nodes = nodes, edges = my_edges) %>%
visOptions(highlightNearest = list(enabled = TRUE, algorithm = "hierarchical",
degree = list(from = 1, to = 1)), nodesIdSelection = TRUE) %>%
visPhysics(stabilization = FALSE, barnesHut = ) %>%
visLayout(improvedLayout = TRUE)